Overview

Dataset statistics

Number of variables28
Number of observations3189
Missing cells825
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory697.7 KiB
Average record size in memory224.0 B

Variable types

Categorical3
Numeric20
Text2
Boolean3

Alerts

councildistrictcode is highly overall correlated with neighborhoodHigh correlation
propertygfatotal is highly overall correlated with propertygfabuilding and 4 other fieldsHigh correlation
propertygfabuilding is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
largestpropertyusetypegfa is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
energystarscore is highly overall correlated with sourceeuiwn_kbtu_sfHigh correlation
siteeuiwn_kbtu_sf is highly overall correlated with sourceeuiwn_kbtu_sf and 3 other fieldsHigh correlation
sourceeuiwn_kbtu_sf is highly overall correlated with energystarscore and 3 other fieldsHigh correlation
siteenergyuse_kbtu is highly overall correlated with propertygfatotal and 6 other fieldsHigh correlation
siteenergyusewn_kbtu is highly overall correlated with propertygfatotal and 6 other fieldsHigh correlation
totalghgemissions is highly overall correlated with propertygfatotal and 6 other fieldsHigh correlation
latitude is highly overall correlated with neighborhoodHigh correlation
source_site is highly overall correlated with totalghgemissions and 1 other fieldsHigh correlation
source_wn is highly overall correlated with site_wn and 1 other fieldsHigh correlation
site_wn is highly overall correlated with source_wnHigh correlation
buildingtype is highly overall correlated with primarypropertytypeHigh correlation
primarypropertytype is highly overall correlated with buildingtypeHigh correlation
neighborhood is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
electricity is highly overall correlated with source_wnHigh correlation
naturalgas is highly overall correlated with source_siteHigh correlation
steam is highly imbalanced (76.1%)Imbalance
electricity is highly imbalanced (99.2%)Imbalance
energystarscore has 803 (25.2%) missing valuesMissing
totalghgemissions is highly skewed (γ1 = 20.23067068)Skewed
siteenergyuse_kbtu has unique valuesUnique
siteenergyusewn_kbtu has unique valuesUnique
numberofbuildings has 91 (2.9%) zerosZeros
propertygfaparking has 2705 (84.8%) zerosZeros

Reproduction

Analysis started2023-09-04 16:45:49.293910
Analysis finished2023-09-04 16:47:14.230722
Duration1 minute and 24.94 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

buildingtype
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
NonResidential
1421 
Multifamily LR (1-4)
979 
Multifamily MR (5-9)
567 
Multifamily HR (10+)
 
107
Nonresidential COS
 
81
Other values (3)
 
34

Length

Max length20
Median length20
Mean length17.164315
Min length6

Characters and Unicode

Total characters54737
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential

Common Values

ValueCountFrequency (%)
NonResidential 1421
44.6%
Multifamily LR (1-4) 979
30.7%
Multifamily MR (5-9) 567
 
17.8%
Multifamily HR (10+) 107
 
3.4%
Nonresidential COS 81
 
2.5%
Campus 23
 
0.7%
SPS-District K-12 10
 
0.3%
Nonresidential WA 1
 
< 0.1%

Length

2023-09-04T18:47:14.411163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:47:14.825268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1653
25.1%
nonresidential 1503
22.8%
lr 979
14.9%
1-4 979
14.9%
mr 567
 
8.6%
5-9 567
 
8.6%
hr 107
 
1.6%
10 107
 
1.6%
cos 81
 
1.2%
campus 23
 
0.3%
Other values (3) 21
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 6332
 
11.6%
l 4809
 
8.8%
3398
 
6.2%
a 3179
 
5.8%
t 3176
 
5.8%
R 3074
 
5.6%
n 3006
 
5.5%
e 3006
 
5.5%
M 2220
 
4.1%
u 1676
 
3.1%
Other values (30) 20861
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34833
63.6%
Uppercase Letter 8201
 
15.0%
Space Separator 3398
 
6.2%
Decimal Number 3326
 
6.1%
Close Punctuation 1653
 
3.0%
Open Punctuation 1653
 
3.0%
Dash Punctuation 1566
 
2.9%
Math Symbol 107
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6332
18.2%
l 4809
13.8%
a 3179
9.1%
t 3176
9.1%
n 3006
8.6%
e 3006
8.6%
u 1676
 
4.8%
m 1676
 
4.8%
y 1653
 
4.7%
f 1653
 
4.7%
Other values (6) 4667
13.4%
Uppercase Letter
ValueCountFrequency (%)
R 3074
37.5%
M 2220
27.1%
N 1503
18.3%
L 979
 
11.9%
H 107
 
1.3%
C 104
 
1.3%
S 101
 
1.2%
O 81
 
1.0%
P 10
 
0.1%
D 10
 
0.1%
Other values (3) 12
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1096
33.0%
4 979
29.4%
5 567
17.0%
9 567
17.0%
0 107
 
3.2%
2 10
 
0.3%
Space Separator
ValueCountFrequency (%)
3398
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1653
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1566
100.0%
Math Symbol
ValueCountFrequency (%)
+ 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43034
78.6%
Common 11703
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6332
14.7%
l 4809
11.2%
a 3179
 
7.4%
t 3176
 
7.4%
R 3074
 
7.1%
n 3006
 
7.0%
e 3006
 
7.0%
M 2220
 
5.2%
u 1676
 
3.9%
m 1676
 
3.9%
Other values (19) 10880
25.3%
Common
ValueCountFrequency (%)
3398
29.0%
) 1653
14.1%
( 1653
14.1%
- 1566
13.4%
1 1096
 
9.4%
4 979
 
8.4%
5 567
 
4.8%
9 567
 
4.8%
0 107
 
0.9%
+ 107
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6332
 
11.6%
l 4809
 
8.8%
3398
 
6.2%
a 3179
 
5.8%
t 3176
 
5.8%
R 3074
 
5.6%
n 3006
 
5.5%
e 3006
 
5.5%
M 2220
 
4.1%
u 1676
 
3.1%
Other values (30) 20861
38.1%

primarypropertytype
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
Low-Rise Multifamily
951 
Mid-Rise Multifamily
550 
Small- and Mid-Sized Office
286 
Other
250 
Warehouse
185 
Other values (18)
967 

Length

Max length27
Median length22
Mean length17.360301
Min length5

Characters and Unicode

Total characters55362
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily 951
29.8%
Mid-Rise Multifamily 550
17.2%
Small- and Mid-Sized Office 286
 
9.0%
Other 250
 
7.8%
Warehouse 185
 
5.8%
Large Office 165
 
5.2%
Mixed Use Property 131
 
4.1%
High-Rise Multifamily 102
 
3.2%
Retail Store 85
 
2.7%
Hotel 76
 
2.4%
Other values (13) 408
12.8%

Length

2023-09-04T18:47:15.138435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1603
24.2%
low-rise 951
14.3%
mid-rise 550
 
8.3%
office 490
 
7.4%
small 286
 
4.3%
and 286
 
4.3%
mid-sized 286
 
4.3%
other 250
 
3.8%
warehouse 197
 
3.0%
large 165
 
2.5%
Other values (28) 1573
23.7%

Most occurring characters

ValueCountFrequency (%)
i 7369
 
13.3%
e 4409
 
8.0%
l 4207
 
7.6%
3448
 
6.2%
a 2955
 
5.3%
t 2711
 
4.9%
f 2623
 
4.7%
M 2609
 
4.7%
- 2253
 
4.1%
s 2118
 
3.8%
Other values (33) 20660
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41293
74.6%
Uppercase Letter 8229
 
14.9%
Space Separator 3448
 
6.2%
Dash Punctuation 2253
 
4.1%
Decimal Number 100
 
0.2%
Other Punctuation 39
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7369
17.8%
e 4409
10.7%
l 4207
10.2%
a 2955
 
7.2%
t 2711
 
6.6%
f 2623
 
6.4%
s 2118
 
5.1%
m 2014
 
4.9%
u 1947
 
4.7%
y 1943
 
4.7%
Other values (14) 8997
21.8%
Uppercase Letter
ValueCountFrequency (%)
M 2609
31.7%
R 1735
21.1%
L 1126
13.7%
S 884
 
10.7%
O 740
 
9.0%
W 264
 
3.2%
H 211
 
2.6%
U 153
 
1.9%
C 139
 
1.7%
P 131
 
1.6%
Other values (4) 237
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Space Separator
ValueCountFrequency (%)
3448
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2253
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49522
89.5%
Common 5840
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7369
14.9%
e 4409
 
8.9%
l 4207
 
8.5%
a 2955
 
6.0%
t 2711
 
5.5%
f 2623
 
5.3%
M 2609
 
5.3%
s 2118
 
4.3%
m 2014
 
4.1%
u 1947
 
3.9%
Other values (28) 16560
33.4%
Common
ValueCountFrequency (%)
3448
59.0%
- 2253
38.6%
1 50
 
0.9%
2 50
 
0.9%
/ 39
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7369
 
13.3%
e 4409
 
8.0%
l 4207
 
7.6%
3448
 
6.2%
a 2955
 
5.3%
t 2711
 
4.9%
f 2623
 
4.7%
M 2609
 
4.7%
- 2253
 
4.1%
s 2118
 
3.8%
Other values (33) 20660
37.3%

councildistrictcode
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4772656
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:15.388837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1152195
Coefficient of variation (CV)0.47243557
Kurtosis-1.4465961
Mean4.4772656
Median Absolute Deviation (MAD)2
Skewness-0.087930759
Sum14278
Variance4.4741536
MonotonicityNot monotonic
2023-09-04T18:47:15.652041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1003
31.5%
3 569
17.8%
2 473
14.8%
4 343
 
10.8%
5 319
 
10.0%
1 251
 
7.9%
6 231
 
7.2%
ValueCountFrequency (%)
1 251
 
7.9%
2 473
14.8%
3 569
17.8%
4 343
 
10.8%
5 319
 
10.0%
6 231
 
7.2%
7 1003
31.5%
ValueCountFrequency (%)
7 1003
31.5%
6 231
 
7.2%
5 319
 
10.0%
4 343
 
10.8%
3 569
17.8%
2 473
14.8%
1 251
 
7.9%

neighborhood
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
DOWNTOWN
549 
EAST
439 
MAGNOLIA / QUEEN ANNE
410 
GREATER DUWAMISH
357 
NORTHEAST
261 
Other values (8)
1173 

Length

Max length21
Median length10
Mean length10.142051
Min length4

Characters and Unicode

Total characters32343
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN 549
17.2%
EAST 439
13.8%
MAGNOLIA / QUEEN ANNE 410
12.9%
GREATER DUWAMISH 357
11.2%
NORTHEAST 261
8.2%
LAKE UNION 242
7.6%
NORTHWEST 207
 
6.5%
NORTH 176
 
5.5%
SOUTHWEST 146
 
4.6%
BALLARD 126
 
4.0%
Other values (3) 276
8.7%

Length

2023-09-04T18:47:15.951819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 549
10.9%
east 439
 
8.7%
magnolia 410
 
8.2%
410
 
8.2%
queen 410
 
8.2%
anne 410
 
8.2%
greater 357
 
7.1%
duwamish 357
 
7.1%
northeast 261
 
5.2%
union 242
 
4.8%
Other values (8) 1173
23.4%

Most occurring characters

ValueCountFrequency (%)
N 3987
12.3%
E 3592
11.1%
A 3337
10.3%
T 3026
9.4%
O 2618
 
8.1%
1829
 
5.7%
W 1808
 
5.6%
S 1712
 
5.3%
R 1682
 
5.2%
U 1233
 
3.8%
Other values (11) 7519
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 30104
93.1%
Space Separator 1829
 
5.7%
Other Punctuation 410
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 3987
13.2%
E 3592
11.9%
A 3337
11.1%
T 3026
10.1%
O 2618
8.7%
W 1808
 
6.0%
S 1712
 
5.7%
R 1682
 
5.6%
U 1233
 
4.1%
H 1225
 
4.1%
Other values (9) 5884
19.5%
Space Separator
ValueCountFrequency (%)
1829
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30104
93.1%
Common 2239
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 3987
13.2%
E 3592
11.9%
A 3337
11.1%
T 3026
10.1%
O 2618
8.7%
W 1808
 
6.0%
S 1712
 
5.7%
R 1682
 
5.6%
U 1233
 
4.1%
H 1225
 
4.1%
Other values (9) 5884
19.5%
Common
ValueCountFrequency (%)
1829
81.7%
/ 410
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 3987
12.3%
E 3592
11.1%
A 3337
10.3%
T 3026
9.4%
O 2618
 
8.1%
1829
 
5.7%
W 1808
 
5.6%
S 1712
 
5.3%
R 1682
 
5.2%
U 1233
 
3.8%
Other values (11) 7519
23.2%

numberofbuildings
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0768266
Minimum0
Maximum27
Zeros91
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:16.274776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94851424
Coefficient of variation (CV)0.88084214
Kurtosis320.32293
Mean1.0768266
Median Absolute Deviation (MAD)0
Skewness15.338562
Sum3434
Variance0.89967926
MonotonicityNot monotonic
2023-09-04T18:47:16.620079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3001
94.1%
0 91
 
2.9%
2 35
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
8 3
 
0.1%
14 2
 
0.1%
9 2
 
0.1%
Other values (6) 7
 
0.2%
ValueCountFrequency (%)
0 91
 
2.9%
1 3001
94.1%
2 35
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
0.1%
11 1
 
< 0.1%
10 2
 
0.1%
9 2
 
0.1%
8 3
0.1%
7 1
 
< 0.1%
6 5
0.2%

numberoffloors
Real number (ℝ)

Distinct50
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.786767
Minimum0
Maximum99
Zeros15
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:16.940553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.5580941
Coefficient of variation (CV)1.1611374
Kurtosis55.622397
Mean4.786767
Median Absolute Deviation (MAD)1
Skewness5.9076641
Sum15265
Variance30.89241
MonotonicityNot monotonic
2023-09-04T18:47:17.281745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 664
20.8%
3 648
20.3%
1 423
13.3%
2 395
12.4%
6 298
9.3%
5 289
9.1%
7 142
 
4.5%
8 63
 
2.0%
10 32
 
1.0%
11 32
 
1.0%
Other values (40) 203
 
6.4%
ValueCountFrequency (%)
0 15
 
0.5%
1 423
13.3%
2 395
12.4%
3 648
20.3%
4 664
20.8%
5 289
9.1%
6 298
9.3%
7 142
 
4.5%
8 63
 
2.0%
9 18
 
0.6%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 2
 
0.1%

propertygfatotal
Real number (ℝ)

HIGH CORRELATION 

Distinct3021
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92572.002
Minimum11285
Maximum2200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:17.668550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21282.8
Q128256
median43728
Q391091
95-th percentile321219.8
Maximum2200000
Range2188715
Interquartile range (IQR)62835

Descriptive statistics

Standard deviation152048.2
Coefficient of variation (CV)1.6424859
Kurtosis49.28716
Mean92572.002
Median Absolute Deviation (MAD)19512
Skewness5.8871601
Sum2.9521211 × 108
Variance2.3118657 × 1010
MonotonicityNot monotonic
2023-09-04T18:47:18.055677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.3%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
22320 4
 
0.1%
30720 4
 
0.1%
30240 4
 
0.1%
30000 3
 
0.1%
22344 3
 
0.1%
Other values (3011) 3134
98.3%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
14101 1
< 0.1%
16000 1
< 0.1%
16300 1
< 0.1%
16795 1
< 0.1%
18258 1
< 0.1%
ValueCountFrequency (%)
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%
1354987 1
< 0.1%

propertygfaparking
Real number (ℝ)

ZEROS 

Distinct476
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8277.5967
Minimum0
Maximum512608
Zeros2705
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:18.424496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile49415
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33059.323
Coefficient of variation (CV)3.993831
Kurtosis56.706924
Mean8277.5967
Median Absolute Deviation (MAD)0
Skewness6.5361756
Sum26397256
Variance1.0929188 × 109
MonotonicityNot monotonic
2023-09-04T18:47:18.822971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2705
84.8%
13320 3
 
0.1%
12960 2
 
0.1%
25800 2
 
0.1%
22000 2
 
0.1%
30000 2
 
0.1%
10800 2
 
0.1%
100176 2
 
0.1%
20416 2
 
0.1%
15576 1
 
< 0.1%
Other values (466) 466
 
14.6%
ValueCountFrequency (%)
0 2705
84.8%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

propertygfabuilding
Real number (ℝ)

HIGH CORRELATION 

Distinct3017
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84294.405
Minimum3636
Maximum2200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:19.109201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21019.6
Q127533
median42720
Q384385
95-th percentile283851.6
Maximum2200000
Range2196364
Interquartile range (IQR)56852

Descriptive statistics

Standard deviation134911.21
Coefficient of variation (CV)1.6004765
Kurtosis56.930799
Mean84294.405
Median Absolute Deviation (MAD)18600
Skewness6.2166042
Sum2.6881486 × 108
Variance1.8201035 × 1010
MonotonicityNot monotonic
2023-09-04T18:47:19.499223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.3%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30720 4
 
0.1%
22320 4
 
0.1%
30240 4
 
0.1%
20000 3
 
0.1%
21900 3
 
0.1%
Other values (3007) 3134
98.3%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
14101 1
< 0.1%
ValueCountFrequency (%)
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%
1195387 1
< 0.1%
1172127 1
< 0.1%
Distinct457
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:19.858596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length255
Median length166
Mean length26.374098
Min length5

Characters and Unicode

Total characters84107
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)9.6%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
multifamily 1659
17.5%
housing 1659
17.5%
parking 1065
11.2%
office 937
9.9%
store 454
 
4.8%
other 411
 
4.3%
retail 389
 
4.1%
warehouse 273
 
2.9%
non-refrigerated 256
 
2.7%
178
 
1.9%
Other values (96) 2193
23.1%
2023-09-04T18:47:20.599337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9119
 
10.8%
6285
 
7.5%
e 5541
 
6.6%
a 5116
 
6.1%
t 4808
 
5.7%
l 4733
 
5.6%
u 4143
 
4.9%
r 4140
 
4.9%
n 4069
 
4.8%
f 3872
 
4.6%
Other values (42) 32281
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64194
76.3%
Uppercase Letter 9918
 
11.8%
Space Separator 6285
 
7.5%
Other Punctuation 2984
 
3.5%
Dash Punctuation 536
 
0.6%
Decimal Number 116
 
0.1%
Open Punctuation 37
 
< 0.1%
Close Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9119
14.2%
e 5541
 
8.6%
a 5116
 
8.0%
t 4808
 
7.5%
l 4733
 
7.4%
u 4143
 
6.5%
r 4140
 
6.4%
n 4069
 
6.3%
f 3872
 
6.0%
o 3732
 
5.8%
Other values (12) 14921
23.2%
Uppercase Letter
ValueCountFrequency (%)
H 1858
18.7%
M 1808
18.2%
O 1370
13.8%
P 1210
12.2%
R 936
9.4%
S 915
9.2%
W 347
 
3.5%
C 330
 
3.3%
N 262
 
2.6%
F 219
 
2.2%
Other values (11) 663
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 2619
87.8%
/ 353
 
11.8%
& 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 58
50.0%
2 58
50.0%
Space Separator
ValueCountFrequency (%)
6285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74112
88.1%
Common 9995
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9119
 
12.3%
e 5541
 
7.5%
a 5116
 
6.9%
t 4808
 
6.5%
l 4733
 
6.4%
u 4143
 
5.6%
r 4140
 
5.6%
n 4069
 
5.5%
f 3872
 
5.2%
o 3732
 
5.0%
Other values (33) 24839
33.5%
Common
ValueCountFrequency (%)
6285
62.9%
, 2619
26.2%
- 536
 
5.4%
/ 353
 
3.5%
1 58
 
0.6%
2 58
 
0.6%
( 37
 
0.4%
) 37
 
0.4%
& 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9119
 
10.8%
6285
 
7.5%
e 5541
 
6.6%
a 5116
 
6.1%
t 4808
 
5.7%
l 4733
 
5.6%
u 4143
 
4.9%
r 4140
 
4.9%
n 4069
 
4.8%
f 3872
 
4.6%
Other values (42) 32281
38.4%
Distinct55
Distinct (%)1.7%
Missing11
Missing (%)0.3%
Memory size25.0 KiB
2023-09-04T18:47:21.025130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.433606
Min length5

Characters and Unicode

Total characters52226
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1621
27.9%
housing 1621
27.9%
office 525
 
9.0%
warehouse 209
 
3.6%
non-refrigerated 197
 
3.4%
other 174
 
3.0%
store 133
 
2.3%
facility 96
 
1.6%
retail 93
 
1.6%
86
 
1.5%
Other values (78) 1064
18.3%
2023-09-04T18:47:21.541761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6637
 
12.7%
l 3932
 
7.5%
u 3696
 
7.1%
t 3015
 
5.8%
e 2965
 
5.7%
f 2921
 
5.6%
o 2820
 
5.4%
a 2736
 
5.2%
2641
 
5.1%
n 2331
 
4.5%
Other values (41) 18532
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42793
81.9%
Uppercase Letter 6113
 
11.7%
Space Separator 2641
 
5.1%
Dash Punctuation 353
 
0.7%
Other Punctuation 192
 
0.4%
Decimal Number 100
 
0.2%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6637
15.5%
l 3932
9.2%
u 3696
 
8.6%
t 3015
 
7.0%
e 2965
 
6.9%
f 2921
 
6.8%
o 2820
 
6.6%
a 2736
 
6.4%
n 2331
 
5.4%
s 2135
 
5.0%
Other values (11) 9605
22.4%
Uppercase Letter
ValueCountFrequency (%)
H 1751
28.6%
M 1706
27.9%
O 711
11.6%
R 384
 
6.3%
S 377
 
6.2%
W 277
 
4.5%
N 197
 
3.2%
C 191
 
3.1%
F 107
 
1.8%
D 89
 
1.5%
Other values (11) 323
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 162
84.4%
, 20
 
10.4%
& 10
 
5.2%
Decimal Number
ValueCountFrequency (%)
2 50
50.0%
1 50
50.0%
Space Separator
ValueCountFrequency (%)
2641
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 353
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48906
93.6%
Common 3320
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6637
13.6%
l 3932
 
8.0%
u 3696
 
7.6%
t 3015
 
6.2%
e 2965
 
6.1%
f 2921
 
6.0%
o 2820
 
5.8%
a 2736
 
5.6%
n 2331
 
4.8%
s 2135
 
4.4%
Other values (32) 15718
32.1%
Common
ValueCountFrequency (%)
2641
79.5%
- 353
 
10.6%
/ 162
 
4.9%
2 50
 
1.5%
1 50
 
1.5%
, 20
 
0.6%
( 17
 
0.5%
) 17
 
0.5%
& 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6637
 
12.7%
l 3932
 
7.5%
u 3696
 
7.1%
t 3015
 
5.8%
e 2965
 
5.7%
f 2921
 
5.6%
o 2820
 
5.4%
a 2736
 
5.2%
2641
 
5.1%
n 2331
 
4.5%
Other values (41) 18532
35.5%

largestpropertyusetypegfa
Real number (ℝ)

HIGH CORRELATION 

Distinct2959
Distinct (%)93.1%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean76314.751
Minimum5656
Maximum1719643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:21.843908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17412.2
Q124961.25
median38929
Q375543
95-th percentile245059.8
Maximum1719643
Range1713987
Interquartile range (IQR)50581.75

Descriptive statistics

Standard deviation124692.65
Coefficient of variation (CV)1.6339259
Kurtosis54.671926
Mean76314.751
Median Absolute Deviation (MAD)16773.5
Skewness6.2266668
Sum2.4252828 × 108
Variance1.5548256 × 1010
MonotonicityNot monotonic
2023-09-04T18:47:22.046766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 9
 
0.3%
22000 7
 
0.2%
21600 7
 
0.2%
20000 7
 
0.2%
30000 6
 
0.2%
24288 5
 
0.2%
28800 5
 
0.2%
15000 5
 
0.2%
45000 5
 
0.2%
36000 5
 
0.2%
Other values (2949) 3117
97.7%
(Missing) 11
 
0.3%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
7387 1
< 0.1%
7501 1
< 0.1%
7583 1
< 0.1%
7758 1
< 0.1%
8061 1
< 0.1%
ValueCountFrequency (%)
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
1350182 1
< 0.1%
1314475 1
< 0.1%
1191115 1
< 0.1%
1172127 1
< 0.1%
1011135 1
< 0.1%
1010135 1
< 0.1%

energystarscore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)4.2%
Missing803
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean67.231769
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:22.570172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.25
Q152
median74
Q389
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.997359
Coefficient of variation (CV)0.40155657
Kurtosis-0.3079605
Mean67.231769
Median Absolute Deviation (MAD)18
Skewness-0.81502344
Sum160415
Variance728.85737
MonotonicityNot monotonic
2023-09-04T18:47:22.924855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 90
 
2.8%
98 71
 
2.2%
96 61
 
1.9%
89 54
 
1.7%
93 50
 
1.6%
95 49
 
1.5%
91 47
 
1.5%
92 47
 
1.5%
99 47
 
1.5%
81 46
 
1.4%
Other values (90) 1824
57.2%
(Missing) 803
25.2%
ValueCountFrequency (%)
1 33
1.0%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.2%
5 8
 
0.3%
6 8
 
0.3%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.2%
10 10
 
0.3%
ValueCountFrequency (%)
100 90
2.8%
99 47
1.5%
98 71
2.2%
97 45
1.4%
96 61
1.9%
95 49
1.5%
94 45
1.4%
93 50
1.6%
92 47
1.5%
91 47
1.5%

siteeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1081
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.146911
Minimum1.5
Maximum834.40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:23.214547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile19.839999
Q129.700001
median41.599998
Q365.599998
95-th percentile152.58
Maximum834.40002
Range832.90002
Interquartile range (IQR)35.899998

Descriptive statistics

Standard deviation57.621877
Coefficient of variation (CV)0.99097056
Kurtosis37.937931
Mean58.146911
Median Absolute Deviation (MAD)14.600002
Skewness4.8682816
Sum185430.5
Variance3320.2807
MonotonicityNot monotonic
2023-09-04T18:47:23.418471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.5 16
 
0.5%
30.79999924 15
 
0.5%
32.20000076 14
 
0.4%
29 14
 
0.4%
30.20000076 14
 
0.4%
27.89999962 13
 
0.4%
31.39999962 13
 
0.4%
24.60000038 12
 
0.4%
28.10000038 12
 
0.4%
25.10000038 12
 
0.4%
Other values (1071) 3054
95.8%
ValueCountFrequency (%)
1.5 1
< 0.1%
2.099999905 1
< 0.1%
2.299999952 1
< 0.1%
3 1
< 0.1%
3.200000048 1
< 0.1%
3.5 1
< 0.1%
3.599999905 2
0.1%
4 1
< 0.1%
4.300000191 2
0.1%
4.599999905 1
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
694.7000122 1
< 0.1%
693.0999756 1
< 0.1%
639.7999878 1
< 0.1%
593.5999756 1
< 0.1%
468.7000122 1
< 0.1%
467 1
< 0.1%
460.1000061 1
< 0.1%
426.6000061 1
< 0.1%

sourceeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1655
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.89608
Minimum0
Maximum2620
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:23.610467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.440001
Q179.5
median103.1
Q3151.60001
95-th percentile360.08
Maximum2620
Range2620
Interquartile range (IQR)72.100006

Descriptive statistics

Standard deviation139.51542
Coefficient of variation (CV)0.99020087
Kurtosis79.533873
Mean140.89608
Median Absolute Deviation (MAD)29.5
Skewness6.6802915
Sum449317.6
Variance19464.553
MonotonicityNot monotonic
2023-09-04T18:47:23.819018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.59999847 9
 
0.3%
75.5 8
 
0.3%
98.90000153 8
 
0.3%
102.4000015 8
 
0.3%
93.59999847 8
 
0.3%
104.5999985 8
 
0.3%
83.5 8
 
0.3%
84.90000153 7
 
0.2%
80.69999695 7
 
0.2%
89.09999847 7
 
0.2%
Other values (1645) 3111
97.6%
ValueCountFrequency (%)
0 1
< 0.1%
4.599999905 1
< 0.1%
6.599999905 1
< 0.1%
6.900000095 1
< 0.1%
7.400000095 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
10 1
< 0.1%
10.30000019 1
< 0.1%
11.19999981 1
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2008 1
< 0.1%
1527.300049 1
< 0.1%
1195.099976 1
< 0.1%
1138.400024 1
< 0.1%
1001 1
< 0.1%
954 1
< 0.1%
919.2999878 1
< 0.1%

siteenergyuse_kbtu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5239344.8
Minimum57133.199
Maximum4.4838531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:24.036072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57133.199
5-th percentile521155.12
Q1936616.5
median1802805.1
Q34230514
95-th percentile18336010
Maximum4.4838531 × 108
Range4.4832818 × 108
Interquartile range (IQR)3293897.5

Descriptive statistics

Standard deviation15954971
Coefficient of variation (CV)3.0452225
Kurtosis303.93997
Mean5239344.8
Median Absolute Deviation (MAD)1062574.8
Skewness14.645387
Sum1.6708271 × 1010
Variance2.5456109 × 1014
MonotonicityNot monotonic
2023-09-04T18:47:24.236403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.5 1
 
< 0.1%
421389.4063 1
 
< 0.1%
6714540 1
 
< 0.1%
4653535 1
 
< 0.1%
8163413 1
 
< 0.1%
415364.5938 1
 
< 0.1%
561473.875 1
 
< 0.1%
905750.375 1
 
< 0.1%
2253647.75 1
 
< 0.1%
816300.5 1
 
< 0.1%
Other values (3179) 3179
99.7%
ValueCountFrequency (%)
57133.19922 1
< 0.1%
79711.79688 1
< 0.1%
90558.70313 1
< 0.1%
97690.39844 1
< 0.1%
106918 1
< 0.1%
111969.7031 1
< 0.1%
113130 1
< 0.1%
116486.6016 1
< 0.1%
117438.3984 1
< 0.1%
123767.2031 1
< 0.1%
ValueCountFrequency (%)
448385312 1
< 0.1%
293090784 1
< 0.1%
291614432 1
< 0.1%
274682208 1
< 0.1%
253832464 1
< 0.1%
163945984 1
< 0.1%
143423024 1
< 0.1%
131373880 1
< 0.1%
114648520 1
< 0.1%
102673696 1
< 0.1%

siteenergyusewn_kbtu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5415193.6
Minimum58114.199
Maximum4.7161386 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:24.443907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum58114.199
5-th percentile548934.62
Q1992151.88
median1915150.8
Q34431765.5
95-th percentile18803703
Maximum4.7161386 × 108
Range4.7155574 × 108
Interquartile range (IQR)3439613.6

Descriptive statistics

Standard deviation16337191
Coefficient of variation (CV)3.0169173
Kurtosis319.64302
Mean5415193.6
Median Absolute Deviation (MAD)1126665.1
Skewness14.955438
Sum1.7269052 × 1010
Variance2.6690382 × 1014
MonotonicityNot monotonic
2023-09-04T18:47:24.642244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7456910 1
 
< 0.1%
455648.9063 1
 
< 0.1%
6739209 1
 
< 0.1%
4653535 1
 
< 0.1%
8224244 1
 
< 0.1%
486884.9063 1
 
< 0.1%
630412.625 1
 
< 0.1%
963968.1875 1
 
< 0.1%
2266028.75 1
 
< 0.1%
869296.3125 1
 
< 0.1%
Other values (3179) 3179
99.7%
ValueCountFrequency (%)
58114.19922 1
< 0.1%
79967.89844 1
< 0.1%
90558.70313 1
< 0.1%
98862.89844 1
< 0.1%
109471.7969 1
< 0.1%
116486.6016 1
< 0.1%
116642.5 1
< 0.1%
120610.5 1
< 0.1%
127374 1
< 0.1%
128383.8984 1
< 0.1%
ValueCountFrequency (%)
471613856 1
< 0.1%
296671744 1
< 0.1%
295929888 1
< 0.1%
274725984 1
< 0.1%
257764208 1
< 0.1%
167207104 1
< 0.1%
147299056 1
< 0.1%
137106112 1
< 0.1%
123205560 1
< 0.1%
103985264 1
< 0.1%

steam
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
False
3064 
True
 
125
ValueCountFrequency (%)
False 3064
96.1%
True 125
 
3.9%
2023-09-04T18:47:24.830627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

electricity
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
True
3187 
False
 
2
ValueCountFrequency (%)
True 3187
99.9%
False 2
 
0.1%
2023-09-04T18:47:24.973747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

naturalgas
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
True
2003 
False
1186 
ValueCountFrequency (%)
True 2003
62.8%
False 1186
37.2%
2023-09-04T18:47:25.143700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

totalghgemissions
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2688
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.5715
Minimum0
Maximum16870.98
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:25.300856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.92
Q19.63
median33.92
Q394.02
95-th percentile397.018
Maximum16870.98
Range16870.98
Interquartile range (IQR)84.39

Descriptive statistics

Standard deviation517.18369
Coefficient of variation (CV)4.3617874
Kurtosis526.94894
Mean118.5715
Median Absolute Deviation (MAD)27.85
Skewness20.230671
Sum378124.52
Variance267478.97
MonotonicityNot monotonic
2023-09-04T18:47:25.503250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.95 7
 
0.2%
4.2 6
 
0.2%
4.74 5
 
0.2%
4.15 5
 
0.2%
9.29 5
 
0.2%
3.63 5
 
0.2%
5.46 5
 
0.2%
5.07 5
 
0.2%
4.8 5
 
0.2%
4.52 5
 
0.2%
Other values (2678) 3136
98.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.4 1
< 0.1%
0.63 1
< 0.1%
0.68 1
< 0.1%
0.75 1
< 0.1%
0.79 1
< 0.1%
0.81 1
< 0.1%
0.82 1
< 0.1%
0.86 1
< 0.1%
0.87 1
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%
3243.48 1
< 0.1%

zipcode
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98116.889
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:25.712461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.65661
Coefficient of variation (CV)0.00019014677
Kurtosis10.597687
Mean98116.889
Median Absolute Deviation (MAD)10
Skewness1.9765428
Sum3.1289476 × 108
Variance348.0691
MonotonicityNot monotonic
2023-09-04T18:47:25.922952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 290
 
9.1%
98104 240
 
7.5%
98122 234
 
7.3%
98101 220
 
6.9%
98134 184
 
5.8%
98105 181
 
5.7%
98121 181
 
5.7%
98102 163
 
5.1%
98119 162
 
5.1%
98103 152
 
4.8%
Other values (49) 1182
37.1%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
98028 1
< 0.1%
98033 1
< 0.1%
98040 1
< 0.1%
98053 1
< 0.1%
98070 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 68
2.1%
98198 1
 
< 0.1%
98195 8
 
0.3%
98191 1
 
< 0.1%
98185 1
 
< 0.1%
98181 1
 
< 0.1%
98178 2
 
0.1%
98177 2
 
0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2725
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624576
Minimum47.50224
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:26.123101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.50224
5-th percentile47.54365
Q147.60105
median47.61891
Q347.65698
95-th percentile47.713028
Maximum47.73387
Range0.23163
Interquartile range (IQR)0.05593

Descriptive statistics

Standard deviation0.047059455
Coefficient of variation (CV)0.00098813384
Kurtosis-0.08997194
Mean47.624576
Median Absolute Deviation (MAD)0.02564
Skewness0.16409831
Sum151874.77
Variance0.0022145924
MonotonicityNot monotonic
2023-09-04T18:47:26.332260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246 9
 
0.3%
47.61598 7
 
0.2%
47.62208 6
 
0.2%
47.62395 5
 
0.2%
47.61543 5
 
0.2%
47.52549 5
 
0.2%
47.52254 4
 
0.1%
47.60071 4
 
0.1%
47.61048 4
 
0.1%
47.5829 4
 
0.1%
Other values (2715) 3136
98.3%
ValueCountFrequency (%)
47.50224 1
< 0.1%
47.50959 1
< 0.1%
47.51018 1
< 0.1%
47.51042 1
< 0.1%
47.51098 1
< 0.1%
47.51104 1
< 0.1%
47.51127 2
0.1%
47.51138 1
< 0.1%
47.51168 1
< 0.1%
47.51169 1
< 0.1%
ValueCountFrequency (%)
47.73387 1
< 0.1%
47.73375 1
< 0.1%
47.73368 1
< 0.1%
47.7336 1
< 0.1%
47.73357 1
< 0.1%
47.73351 1
< 0.1%
47.73331 1
< 0.1%
47.73316 1
< 0.1%
47.73315 1
< 0.1%
47.73279 1
< 0.1%

longitude
Real number (ℝ)

Distinct2521
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33512
Minimum-122.41425
Maximum-122.26028
Zeros0
Zeros (%)0.0%
Negative3189
Negative (%)100.0%
Memory size25.0 KiB
2023-09-04T18:47:26.722524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.38643
Q1-122.35041
median-122.33264
Q3-122.3202
95-th percentile-122.29174
Maximum-122.26028
Range0.15397
Interquartile range (IQR)0.03021

Descriptive statistics

Standard deviation0.026567583
Coefficient of variation (CV)-0.00021717053
Kurtosis0.27120526
Mean-122.33512
Median Absolute Deviation (MAD)0.01482
Skewness-0.18031409
Sum-390126.7
Variance0.00070583648
MonotonicityNot monotonic
2023-09-04T18:47:26.977774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29898 8
 
0.3%
-122.35398 7
 
0.2%
-122.33369 6
 
0.2%
-122.32468 6
 
0.2%
-122.32592 5
 
0.2%
-122.33064 5
 
0.2%
-122.32417 5
 
0.2%
-122.33379 5
 
0.2%
-122.31769 5
 
0.2%
-122.32544 4
 
0.1%
Other values (2511) 3133
98.2%
ValueCountFrequency (%)
-122.41425 1
< 0.1%
-122.41182 1
< 0.1%
-122.41178 1
< 0.1%
-122.41169 1
< 0.1%
-122.41037 1
< 0.1%
-122.41036 1
< 0.1%
-122.41031 1
< 0.1%
-122.40976 1
< 0.1%
-122.40974 1
< 0.1%
-122.40901 1
< 0.1%
ValueCountFrequency (%)
-122.26028 1
< 0.1%
-122.26034 1
< 0.1%
-122.26166 2
0.1%
-122.26172 1
< 0.1%
-122.26177 1
< 0.1%
-122.2618 1
< 0.1%
-122.26216 1
< 0.1%
-122.26223 1
< 0.1%
-122.26235 1
< 0.1%
-122.26277 1
< 0.1%

age
Real number (ℝ)

Distinct113
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.308247
Minimum8
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:27.195287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q126
median48
Q375
95-th percentile115
Maximum123
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.147829
Coefficient of variation (CV)0.61036455
Kurtosis-0.87505967
Mean54.308247
Median Absolute Deviation (MAD)24
Skewness0.54234059
Sum173189
Variance1098.7785
MonotonicityNot monotonic
2023-09-04T18:47:27.404965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 66
 
2.1%
9 66
 
2.1%
55 62
 
1.9%
34 60
 
1.9%
35 59
 
1.9%
24 59
 
1.9%
15 58
 
1.8%
33 56
 
1.8%
22 56
 
1.8%
53 55
 
1.7%
Other values (103) 2592
81.3%
ValueCountFrequency (%)
8 34
1.1%
9 66
2.1%
10 51
1.6%
11 35
1.1%
12 15
 
0.5%
13 23
 
0.7%
14 40
1.3%
15 58
1.8%
16 41
1.3%
17 44
1.4%
ValueCountFrequency (%)
123 51
1.6%
122 7
 
0.2%
121 11
 
0.3%
120 3
 
0.1%
119 14
 
0.4%
118 9
 
0.3%
117 18
 
0.6%
116 31
1.0%
115 26
0.8%
114 28
0.9%

source_site
Real number (ℝ)

HIGH CORRELATION 

Distinct2854
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.565758
Minimum0
Maximum13.211111
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:27.611379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4650307
Q12.1259259
median2.6509803
Q33.1385135
95-th percentile3.1439395
Maximum13.211111
Range13.211111
Interquartile range (IQR)1.0125876

Descriptive statistics

Standard deviation0.61958081
Coefficient of variation (CV)0.24148061
Kurtosis26.300415
Mean2.565758
Median Absolute Deviation (MAD)0.48790869
Skewness1.1101919
Sum8182.2023
Variance0.38388038
MonotonicityNot monotonic
2023-09-04T18:47:27.825067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.142857143 7
 
0.2%
3.137184085 5
 
0.2%
3.1390977 5
 
0.2%
3.140077609 5
 
0.2%
3.13620076 5
 
0.2%
3.140939626 5
 
0.2%
3.142276498 5
 
0.2%
3.136363636 5
 
0.2%
3.145038135 4
 
0.1%
3.142372778 4
 
0.1%
Other values (2844) 3139
98.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.4214285895 1
< 0.1%
1.049797122 1
< 0.1%
1.050158691 1
< 0.1%
1.059027806 1
< 0.1%
1.082949271 1
< 0.1%
1.115999985 1
< 0.1%
1.130331751 1
< 0.1%
1.134883703 1
< 0.1%
1.143327853 1
< 0.1%
ValueCountFrequency (%)
13.21111111 1
< 0.1%
5.204283195 1
< 0.1%
4.668176647 1
< 0.1%
3.188596464 1
< 0.1%
3.173913192 1
< 0.1%
3.166666667 1
< 0.1%
3.166666645 1
< 0.1%
3.163636295 1
< 0.1%
3.157894843 1
< 0.1%
3.156249796 1
< 0.1%

source_wn
Real number (ℝ)

HIGH CORRELATION 

Distinct2799
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0432206
Minimum0
Maximum1.2064343
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:28.040448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.0149787
median1.0428101
Q31.0663316
95-th percentile1.101684
Maximum1.2064343
Range1.2064343
Interquartile range (IQR)0.051352941

Descriptive statistics

Standard deviation0.039812487
Coefficient of variation (CV)0.038163056
Kurtosis155.84268
Mean1.0432206
Median Absolute Deviation (MAD)0.025433741
Skewness-6.0059589
Sum3326.8306
Variance0.0015850341
MonotonicityNot monotonic
2023-09-04T18:47:28.385577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 354
 
11.1%
1.076923009 2
 
0.1%
1.032502707 2
 
0.1%
1.044897974 2
 
0.1%
1.032608682 2
 
0.1%
1.036572624 2
 
0.1%
1.087922691 2
 
0.1%
1.048048001 2
 
0.1%
1.04787234 2
 
0.1%
1.03809528 2
 
0.1%
Other values (2789) 2817
88.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.5251303506 1
< 0.1%
0.9626268025 1
< 0.1%
0.9683258313 1
< 0.1%
0.9704236847 1
< 0.1%
0.9731103776 1
< 0.1%
0.9747545165 1
< 0.1%
0.9750633386 1
< 0.1%
0.9752603981 1
< 0.1%
0.9753886791 1
< 0.1%
ValueCountFrequency (%)
1.206434341 1
< 0.1%
1.186868732 1
< 0.1%
1.173652664 1
< 0.1%
1.166666667 1
< 0.1%
1.160937548 1
< 0.1%
1.160535157 1
< 0.1%
1.160164253 1
< 0.1%
1.159695851 1
< 0.1%
1.155844142 1
< 0.1%
1.15384612 1
< 0.1%

site_wn
Real number (ℝ)

HIGH CORRELATION 

Distinct2634
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.05532
Minimum0.69911112
Maximum1.275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-09-04T18:47:28.761014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.69911112
5-th percentile1
Q11.0264296
median1.0518359
Q31.0771513
95-th percentile1.138519
Maximum1.275
Range0.57588885
Interquartile range (IQR)0.050721688

Descriptive statistics

Standard deviation0.043252931
Coefficient of variation (CV)0.040985609
Kurtosis3.4663198
Mean1.05532
Median Absolute Deviation (MAD)0.025345298
Skewness0.45056926
Sum3365.4153
Variance0.0018708161
MonotonicityNot monotonic
2023-09-04T18:47:29.033039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 347
 
10.9%
1.05 4
 
0.1%
1.08 3
 
0.1%
1.079268364 3
 
0.1%
1.048859934 3
 
0.1%
1.065656626 3
 
0.1%
1.041666667 3
 
0.1%
1.056782309 3
 
0.1%
1.071428571 3
 
0.1%
1.044943776 3
 
0.1%
Other values (2624) 2814
88.2%
ValueCountFrequency (%)
0.6991111249 1
< 0.1%
0.7042253615 1
< 0.1%
0.9630872987 1
< 0.1%
0.9635468281 1
< 0.1%
0.9699247696 1
< 0.1%
0.9746695708 1
< 0.1%
0.9762532587 1
< 0.1%
0.9776536522 1
< 0.1%
0.978043942 1
< 0.1%
0.9790660344 1
< 0.1%
ValueCountFrequency (%)
1.274999976 1
< 0.1%
1.239737243 1
< 0.1%
1.228571483 1
< 0.1%
1.223529367 1
< 0.1%
1.215847045 1
< 0.1%
1.212499976 1
< 0.1%
1.203898152 1
< 0.1%
1.200692084 1
< 0.1%
1.200381674 1
< 0.1%
1.196923045 1
< 0.1%

Interactions

2023-09-04T18:47:08.722283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:51.833841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:55.997404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:00.021172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.064983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:07.467293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:11.365300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:14.867752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:19.477688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:23.888826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:28.969313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:32.505718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:36.787224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:41.204177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:45.172959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:48.608153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:52.668409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:57.466011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.003538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:04.440670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:08.951511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:52.121885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:56.172926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:00.364684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.230393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:07.640851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:11.550439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:15.051130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:19.765296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:24.140402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:29.183379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:32.672386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:36.958924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:41.445479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:45.346224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:48.773226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:52.976878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:57.723888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.180632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:04.613278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:09.131577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:52.407430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:56.336160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:00.645164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.394633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:07.807462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:11.724644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:15.285151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:20.059662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:24.424630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:29.349084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:32.838010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:37.145637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:41.699476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:45.508361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:48.936322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:53.236171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:57.920820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.347404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:04.777684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:09.293998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:52.646522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:56.496828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:00.925683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.557179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:08.086731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:11.884083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:15.576776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:20.224689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:24.701904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:29.626238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:32.996014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:37.307584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:41.959693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:45.676012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:49.095719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:53.415774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:58.078728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.515028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:04.947822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:09.459594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:52.928836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:56.655320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:01.204743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.722157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:08.248933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:12.041353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:15.746469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:20.486493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:24.977894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:29.787524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:33.154921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:37.531511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:46:45.840179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:46:58.232058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.678630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:05.114964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:09.624899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:53.092913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:56.824599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:01.489467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:04.893021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:08.419579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:12.209272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:16.046654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:20.746205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:25.183055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:29.958134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:33.320414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:46:42.311774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:46.013930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:46:53.926025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:58.396354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:01.847587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:05.287715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:09.800010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:45:57.057347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:46:08.579957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:12.375609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:16.224711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:21.042102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:25.446397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:30.124774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-04T18:47:12.221885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:55.824175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:45:59.795700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:03.895485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:07.298298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:11.183515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:14.695906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:19.176288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:23.714398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:28.765417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:32.328357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:36.522069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:41.038596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:44.976363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:48.439937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:52.382832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:46:57.206378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:00.832621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:04.267263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:47:08.486873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-04T18:47:29.251230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
councildistrictcodenumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuildinglargestpropertyusetypegfaenergystarscoresiteeuiwn_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtutotalghgemissionszipcodelatitudelongitudeagesource_sitesource_wnsite_wnbuildingtypeprimarypropertytypeneighborhoodsteamelectricitynaturalgas
councildistrictcode1.000-0.0190.3270.1660.1530.1570.1400.0890.0800.0980.1550.1520.129-0.1950.500-0.357-0.0070.004-0.091-0.1060.1450.2540.8790.2130.0000.142
numberofbuildings-0.0191.000-0.0250.0640.0040.0630.0790.039-0.000-0.0060.0510.0520.0440.0190.0540.037-0.039-0.0090.0180.0180.1820.2020.0390.0000.0000.005
numberoffloors0.327-0.0251.0000.4640.2530.4580.4430.158-0.0120.0630.2940.2880.185-0.2300.059-0.106-0.3010.115-0.064-0.1770.2630.2770.1400.2970.0000.045
propertygfatotal0.1660.0640.4641.0000.3560.9830.9270.0810.1680.2090.7620.7580.577-0.095-0.052-0.024-0.313-0.021-0.306-0.2900.1290.1960.0660.2050.0000.089
propertygfaparking0.1530.0040.2530.3561.0000.2320.2830.0210.1850.2380.3130.3080.219-0.1250.016-0.050-0.2420.057-0.252-0.2500.0490.1540.0600.0820.0000.013
propertygfabuilding0.1570.0630.4580.9830.2321.0000.9260.0810.1450.1790.7460.7430.572-0.082-0.062-0.019-0.285-0.031-0.279-0.2650.1330.1930.0630.2320.0000.087
largestpropertyusetypegfa0.1400.0790.4430.9270.2830.9261.0000.0890.1090.1290.7280.7260.562-0.058-0.044-0.016-0.291-0.032-0.241-0.2340.1220.1980.0650.2260.0000.090
energystarscore0.0890.0390.1580.0810.0210.0810.0891.000-0.455-0.523-0.181-0.183-0.112-0.0070.093-0.043-0.087-0.0020.007-0.0330.1130.1180.0580.0000.0000.111
siteeuiwn_kbtu_sf0.080-0.000-0.0120.1680.1850.1450.109-0.4551.0000.8700.6950.6990.722-0.126-0.0860.0470.086-0.435-0.218-0.0820.1360.2710.0550.1200.1050.184
sourceeuiwn_kbtu_sf0.098-0.0060.0630.2090.2380.1790.129-0.5230.8701.0000.6420.6350.479-0.108-0.0550.036-0.039-0.011-0.365-0.3010.1130.2480.0220.0330.0000.071
siteenergyuse_kbtu0.1550.0510.2940.7620.3130.7460.728-0.1810.6950.6421.0000.9990.880-0.121-0.0940.020-0.158-0.290-0.366-0.2710.1340.2910.0420.1970.0000.046
siteenergyusewn_kbtu0.1520.0520.2880.7580.3080.7430.726-0.1830.6990.6350.9991.0000.889-0.121-0.0960.021-0.147-0.310-0.336-0.2370.1420.2990.0410.2130.0000.049
totalghgemissions0.1290.0440.1850.5770.2190.5720.562-0.1120.7220.4790.8800.8891.000-0.131-0.1140.023-0.026-0.667-0.186-0.0230.1160.2680.0000.1810.0000.037
zipcode-0.1950.019-0.230-0.095-0.125-0.082-0.058-0.007-0.126-0.108-0.121-0.121-0.1311.000-0.0450.003-0.0890.0630.0390.0460.0610.0740.2530.1470.0000.082
latitude0.5000.0540.059-0.0520.016-0.062-0.0440.093-0.086-0.055-0.094-0.096-0.114-0.0451.000-0.020-0.1480.0830.0640.0050.1450.2140.6040.2890.0000.159
longitude-0.3570.037-0.106-0.024-0.050-0.019-0.016-0.0430.0470.0360.0200.0210.0230.003-0.0201.0000.050-0.0380.0210.0190.1200.1510.4980.1840.0000.088
age-0.007-0.039-0.301-0.313-0.242-0.285-0.291-0.0870.086-0.039-0.158-0.147-0.026-0.089-0.1480.0501.000-0.1980.2370.2820.1580.1900.1770.1500.0000.343
source_site0.004-0.0090.115-0.0210.057-0.031-0.032-0.002-0.435-0.011-0.290-0.310-0.6670.0630.083-0.038-0.1981.000-0.190-0.3910.1070.1910.0660.1330.2090.710
source_wn-0.0910.018-0.064-0.306-0.252-0.279-0.2410.007-0.218-0.365-0.366-0.336-0.1860.0390.0640.0210.237-0.1901.0000.9010.0000.2070.0810.0300.7060.131
site_wn-0.1060.018-0.177-0.290-0.250-0.265-0.234-0.033-0.082-0.301-0.271-0.237-0.0230.0460.0050.0190.282-0.3910.9011.0000.1610.2900.1310.0390.4980.246
buildingtype0.1450.1820.2630.1290.0490.1330.1220.1130.1360.1130.1340.1420.1160.0610.1450.1200.1580.1070.0000.1611.0000.6750.1930.1910.0000.275
primarypropertytype0.2540.2020.2770.1960.1540.1930.1980.1180.2710.2480.2910.2990.2680.0740.2140.1510.1900.1910.2070.2900.6751.0000.2430.2870.1900.347
neighborhood0.8790.0390.1400.0660.0600.0630.0650.0580.0550.0220.0420.0410.0000.2530.6040.4980.1770.0660.0810.1310.1930.2431.0000.2860.0260.167
steam0.2130.0000.2970.2050.0820.2320.2260.0000.1200.0330.1970.2130.1810.1470.2890.1840.1500.1330.0300.0390.1910.2870.2861.0000.0000.000
electricity0.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.2090.7060.4980.0000.1900.0260.0001.0000.000
naturalgas0.1420.0050.0450.0890.0130.0870.0900.1110.1840.0710.0460.0490.0370.0820.1590.0880.3430.7100.1310.2460.2750.3470.1670.0000.0001.000

Missing values

2023-09-04T18:47:12.531829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T18:47:13.428927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-04T18:47:14.010089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

buildingtypeprimarypropertytypecouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuildinglistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeuiwn_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamelectricitynaturalgastotalghgemissionszipcodelatitudelongitudeagesource_sitesource_wnsite_wn
0NonResidentialHotel7DOWNTOWN11288434088434HotelHotel88434.060.084.300003189.0000007226362.57456910.0YesYesYes249.9898101.047.61220-122.33799962.2419931.0356161.031824
1NonResidentialHotel7DOWNTOWN1111035661506488502Hotel, Parking, RestaurantHotel83880.061.097.900002179.3999948387933.08664479.0NoYesYes295.8698101.047.61317-122.33393271.8324821.0187391.032700
2NonResidentialHotel7DOWNTOWN141956110196718759392HotelHotel756493.043.097.699997244.10000672587024.073937112.0YesYesYes2089.2898101.047.61393-122.33810542.4984651.0090951.017708
3NonResidentialHotel7DOWNTOWN11061320061320HotelHotel61320.056.0113.300003224.0000006794584.06946800.5YesYesYes286.4398101.047.61412-122.33664971.9770521.0360781.022563
4NonResidentialHotel7DOWNTOWN11817558062000113580Hotel, Parking, Swimming PoolHotel123445.075.0118.699997215.60000614172606.014656503.0NoYesYes505.0198121.047.61375-122.34047431.8163441.0198681.033972
5Nonresidential COSOther7DOWNTOWN12972883719860090Police StationPolice Station88830.0NaN141.600006320.50000012086616.012581712.0NoYesYes301.8198101.047.61623-122.33657242.2634181.0132791.040411
6NonResidentialHotel7DOWNTOWN11183008083008HotelHotel81352.027.074.500000154.6999975758795.06062767.5NoYesYes176.1498101.047.61390-122.33283972.0765101.0552521.052260
7NonResidentialOther7DOWNTOWN181027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaN68.800003152.3000036298131.57067881.5YesYesYes221.5198101.047.61327-122.33136972.2136631.0748061.122349
8NonResidentialHotel7DOWNTOWN1151639840163984HotelHotel163984.043.086.599998187.19999713723820.014194054.0NoYesYes392.1698104.047.60294-122.332631192.1616631.0348261.034648
9Multifamily MR (5-9)Mid-Rise Multifamily7DOWNTOWN1663712149662216Multifamily HousingMultifamily Housing56132.01.085.599998187.3999944573777.04807679.5YesYesYes151.1298104.047.60284-122.331841132.1892521.0257251.050307
buildingtypeprimarypropertytypecouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuildinglistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeuiwn_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamelectricitynaturalgastotalghgemissionszipcodelatitudelongitudeagesource_sitesource_wnsite_wn
3179Nonresidential COSOther5NORTH1111285011285Prison/IncarcerationPrison/Incarceration11285.0NaN62.599998148.3999946.456654e+057.059837e+05NoYesYes14.3798125.047.72126-122.29735742.3706071.0600001.094406
3180Nonresidential COSOther6BALLARD1116795016795Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8680.0NaN59.000000129.3999949.366165e+059.905455e+05NoYesYes24.7398107.047.67295-122.392281122.1932201.0269841.057348
3181Nonresidential COSOther6BALLARD1112769012769Fitness Center/Health Club/Gym, Office, Other - Recreation, Swimming PoolOther - Recreation10912.0NaN420.600006638.9000245.117308e+065.370264e+06NoYesYes216.1898111.047.67734-122.37624511.5190201.0336521.049401
3182Nonresidential COSOther3EAST1123445023445Other - RecreationOther - Recreation23445.0NaN286.500000413.2000125.976246e+066.716330e+06NoYesYes259.2298106.047.63228-122.315741111.4422341.0870821.123970
3183Nonresidential COSMixed Use Property3CENTRAL1120050020050Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/ScienceOther - Recreation8108.0NaN99.400002184.6000061.813404e+061.993137e+06NoYesYes60.8198128.647.60775-122.30225291.8571431.0536531.099558
3184Nonresidential COSOther1DELRIDGE1118261018261Other - RecreationOther - Recreation18261.0NaN56.200001136.6000069.320821e+051.025432e+06NoYesYes20.3398124.047.54067-122.37441412.4306051.0841271.101961
3185Nonresidential COSOther2DOWNTOWN1116000016000Other - RecreationOther - Recreation16000.0NaN65.900002118.9000029.502762e+051.053706e+06NoYesYes32.1798119.047.59625-122.32283191.8042491.0411561.109428
3186Nonresidential COSOther7MAGNOLIA / QUEEN ANNE1113157013157Fitness Center/Health Club/Gym, Other - Recreation, Swimming PoolOther - Recreation7583.0NaN460.100006767.7999885.765898e+066.053764e+06NoYesYes223.5498111.047.63644-122.35784491.6687681.0308811.049977
3187Nonresidential COSMixed Use Property1GREATER DUWAMISH1114101014101Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation6601.0NaN55.500000110.8000037.194712e+057.828413e+05NoYesYes22.1198108.047.52832-122.32431341.9963961.0522321.088235
3188Nonresidential COSMixed Use Property2GREATER DUWAMISH1118258018258Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8271.0NaN70.900002123.9000021.152896e+061.293722e+06NoYesYes41.2798119.747.53939-122.29536851.7475321.0699481.123613